Local antinociceptive action of fluoxetine in the rat formalin assay: role of <scp>l</scp>-arginine/nitric oxide/cGMP/K<sub>ATP</sub> channel pathway
Bibliographic record
Abstract
The present study was conducted to evaluate the local antinociceptive actions of fluoxetine, a selective serotonin reuptake inhibitor, and the possible involvement of the l-arginine/NO/cGMP/KATP channel pathway in this effect using the formalin test in rats. To elucidate the underlying mechanisms, animals were pre-treated with l-NAME, aminoguanidine, methylene blue, glibenclamide, l-arginine, sodium nitroprusside, or diazoxide. Local ipsilateral, but not contralateral, administration of fluoxetine (10–300 μg/paw) dose-dependently suppressed flinching number during both early and late phases of the test, and this was comparable with morphine also given peripherally. Pre-treatment with l-NAME, aminoguanidine, methylene blue, or glibenclamide dose-dependently prevented fluoxetine (100 μg/paw)-induced antinociception in the late phase. In contrast, administration of l-arginine, sodium nitroprusside, and diazoxide significantly enhanced the antinociception caused by fluoxetine in the late phase of the test. However, these treatments had no significant effect on the antinociceptive response of fluoxetine in the early phase of the formalin test. Our data demonstrate that local peripheral antinociception of fluoxetine during the late phase of the formalin test could be due to activation of l-arginine/NO/cGMP/KATP channel pathway. The peripheral action of fluoxetine raises the possibility that topical application of this drug (e.g., as a cream, ointment, or jelly) may be a useful method for relieving the inflammatory pain states.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".